Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farm inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map (e.g., global information systems (GIS) type database) including desired application or control rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller for applying a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for plowing; applying seed, fertilizer, insecticides, herbicides; or supplying other inputs. The effect of the inputs may be analyzed by gathering site-specific soil compaction, yield, and moisture content data and correlating this data with farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, can be difficult to correlate with a precise location in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction, topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage parameters and techniques used). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the yield.
Logging spatially-variable characteristic data may be accomplished in several ways. A farmer may walk or drive a vehicle through a field and take measurements at a plurality of locations in the field. These measurements are manually recorded. Locations of the measurement sites may be determined by reference to a map of the field, or from an electronic positioning unit. This technique, however, produces data which is difficult to integrate into an electronic site-specific farming system since the recorded data must be manually transferred to a site-specific farming database. Further, a large sampling of measurements must be made to obtain a significant sample population.
Obtaining spatially-variable characteristic data could be performed using a recording system which correlates location data received by an electronic positioning unit with soil condition data manually entered into the recording system. The system could automatically store the correlated location data and element data into a site-specific database. Such a system, however, would be disadvantageous because it would require data logging to be performed separately from working the field. Logging data separately from harvesting the field or applying farming inputs to the field wastes time, increases fuel costs, increases wear and tear on the vehicle used for data logging, and prevents recording soil condition data concurrent with the time that the field is being worked.
In connection with the real time and concurrent data recording as the field is being worked, and in accordance with the invention, the loads exhibited on the field working tools as well as between the vehicle and implement may be recorded. Typically the resistance of the ground will vary depending on such factors as the hardness of the ground, its material consistency, the depth of penetration of the implement and so forth. Various systems are known for coupling implements to work vehicles such as agricultural tractors. One such system is the three-point hitch commonly found on a variety of off road vehicles, including agricultural tractors, construction tractors, back hoes and the like. In such vehicles, it is generally known that the force or draft load exerted on the hitch.